{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC 曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "X = digits.data\n",
    "y = digits.target.copy()\n",
    "\n",
    "y[digits.target==9] = 1\n",
    "y[digits.target!=9] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train, y_train)\n",
    "decision_scores = log_reg.decision_function(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from playML.metrics import FPR, TPR\n",
    "\n",
    "fprs = []\n",
    "tprs = []\n",
    "thresholds = np.arange(np.min(decision_scores), np.max(decision_scores), 0.1)\n",
    "for threshold in thresholds:\n",
    "    y_predict = np.array(decision_scores >= threshold, dtype='int')\n",
    "    fprs.append(FPR(y_test, y_predict))\n",
    "    tprs.append(TPR(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(fprs, tprs)\n",
    "plt.xlabel('FPR')\n",
    "plt.ylabel('TPR')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### scikit-learn中的ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "fprs, tprs, thresholds = roc_curve(y_test, decision_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
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    }
   ],
   "source": [
    "print(fprs)\n",
    "print(tprs)\n",
    "print(thresholds)\n",
    "print(fprs.shape)\n",
    "print(tprs.shape)\n",
    "print(thresholds.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(fprs, tprs)\n",
    "plt.xlabel('FPR')\n",
    "plt.ylabel('TPR')\n",
    "plt.show()\n",
    "# 一般关注的是该曲线下方的面积数\n",
    "# 如果面积越大，分类算法越好\n",
    "# 因为如果在FPR小的时候TPR就很大的话，那么该曲线就会向左上偏"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(thresholds, fprs, label='FPR')\n",
    "plt.plot(thresholds, tprs, label='FPR')\n",
    "plt.xlabel('Thresholds')\n",
    "plt.ylabel('Rates')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC AUC\n",
    "Area under curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.9823868312757201"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_test, decision_scores)\n",
    "# 最大的面积也就是1，所以值域是(0,1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6.8 64-bit ('anaconda': conda)",
   "language": "python",
   "name": "python36864bitanacondaconda085db2e8b14649f4b32196068f7124e9"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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